[PDF] (Stem and Word) Predictability in Italian verb paradigms: An Entropy

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(Stem and Word) Predictability in Italian verb paradigms: An Entropy-Based Study Exploiting the New ResourceLeFFI

Matteo Pellegrini

1, Alessandra Teresa Cignarella2;3

1. Liceo Statale "Augusto Monti" di Chieri, Italy

2. Dipartimento di Informatica, Universit

`a degli Studi di Torino, Italy

3. PRHLT Research Center, Universitat Polit

`ecnica de Val`encia, Spain pellegrini.matteo@liceomonti.edu.it, cigna@di.unito.it

Abstract

English.In this paper we presentLeFFI,

an inflected lexicon of Italian listing all the available wordforms of 2,053 verbs.

We then use this resource to perform an

entropy-based analysis of the mutual pre- dictability of wordforms within Italian verb paradigms, and compare our findings to the ones of previous work on stem predictabil- ity in Italian verb inflection.

1 Introduction

an influential line of research where predictability within inflectional paradigms is modelled by resort- ing to the notion of morphomic stems - i.e., stems that cannot be considered as bearing any mean- ing, as they appear in groups of cells that do not share a fixed morphosyntactic content. In this per- spective, every lexeme is seen as equipped with a set of indexed stems, that only for regular lexemes are mutually predictable, while for irregular verbs they need to be independently stored. From each of these stems, a fixed set of wordforms can be ob- tained by adding the appropriate inflectional end- ings. An analysis relying on these assumptions was proposed by Maiden (1992) and subsequent work - see Maiden (2018) for a recent survey - to account for the patterns of stem allomorphy that are found in the verbal inflection of Romance languages in general. More detailed implementations of these ideas have then been provided for individual lan- guages, among them Italian (Pirrelli and Battista,

2000; Montermini and Boy

´e, 2012; Montermini

and Bonami, 2013). Another possibility that has been explored in more recent times is tackling the issue of inflectional predictability in terms of pre-

dictions of wordforms from one another, withoutCopyright © 2020 for this paper by its authors. Use per-

mitted under Creative Commons License Attribution 4.0 Inter- national (CC BY 4.0).assuming a given segmentation in stems vs. end- ings, in a fully word-based, abstractive (Blevins,

2016) approach. Within this framework, Ackerman

flectional predictions by means of the information- theoretic notion of conditional entropy. Building on this work, Bonami and Boy

´e (2014) outline a

procedure that allows to compute entropy values estimating the uncertainty in predicting one cell from another one directly from a lexicon of fully inflected wordforms in phonological transcription, using the type frequency of different inflectional patterns to estimate their probability of applica- tion. This method has been applied to French by

Bonami and Boy

´e (2014), to Latin by Pellegrini

(2020), and it has been used for typological com- parison on a small sample of languages by Beni- amine (2018), who also provides a freely available toolkit (Qumin) allowing to perform this computa- tion automatically for any language.

A similar entropy-based analysis has not been

proposed for Italian yet. To be able to use the Qumin toolkit to perform it, it is necessary to have an inflected lexicon listing all the wordforms of a representative number of lexemes in phonological transcription, likee.g. FlexiqueforFrench(Bonami et al., 2014) or LatInflexi for Latin (Pellegrini and Passarotti, 2018). Looking for such a resource for

Italian, we can see that in most lexicons word-

forms are given in orthographic transcription - see e.g. Morph-it! (Zanchetta and Baroni, 2005) and

CoLFIS(Bertinettoetal., 2005). Ontheotherhand,

in PhonItalia (Goslin et al., 2014) there are phono- logical transcriptions, but not all the inflected word- forms of each lexeme are listed. To the best of our knowledge, the only resource providing phonolog- ical transcriptions of the full paradigm of lexemes is GLAFF-IT (Calderone et al., 2017), but due to the way in which it was created, it proves to be too noisy to be used for entropy computations as such.

In this paper, we describe the work that was done

to obtain a smaller, but cleaner version of GLAFF- IT. We then use this resource to perform an entropy- based analysis of predictability in Italian verb in- flection. After briefly describing the methodology, we present our results comparing them with the findings of previous stem-based analyses.

2 The Resource

In order to buildLeFFI(Lessico delleForme

Flesse dell"Italiano), we have firstly consulted

GLAFF-IT, a free machine-readable dictionary

based onWikizionario, the Italian language edition of Wiktionary. It is a morphophonological Italian lexicon which contains a total of 485,135 word- forms among verbs, nouns, adjectives and adverbs, in both orthographic and phonological IPA tran- scription. Since our interest for the present research lies only in verbs, in this step a total of 411,770 ver- bal forms in phonological transcription have been extracted from GLAFF-IT, together with the cita- tion form (the infinitive) of the lexeme they belong to, thusresultinginalistofthecompleteparadigms of 7,552 verbs. To indicate the morphosyntactic properties expressed by each wordform, we use the notation of the Leipzig Glossing Rules (Comrie et al., 2008), both in our resource and in the examples shown in this paper.

Due to the large amount of manual work needed

in order to obtain our resource, for the time be- ing we have decided to focus only on a fraction of this list. So as not to lose quantitatively rele- vant data, our selection was based on the frequency of lexemes, as reported in the CoLFIS frequency lexicon. We have thus crossed the list of 7,552 verbs extracted from GLAFF-IT with the 5,193 ver- bal lexemes contained in CoLFIS, and kept only the ones with a frequency higher than 10. The re- sulting dataset, listing the 53 available, non- periphrastic cells of 2,053 verbs, is still large enough to allow for reasonably safe generalizations on Italian verb inflection.

After these automatic steps, several manual

changes have been made in order to obtain the cur- rentversionofourresource. Firstly, itshouldbeno- ticed that many of the phonological transcriptions provided by GLAFF-IT are obtained automatically from the orthographic form. In some cases, how- ever, it is not possible to infer a precise phono- logical transcription from orthography alone, be- cause some graphemes can correspond to different phonemes. In such cases, the phonological tran- scriptions provided by GLAFF-IT are underspec-ified: for instance, the symbol E is used for the graphemehei, that can correspond to /e/ or /E/, and similarly O forhoi(/o/ or /O/), S forhsi(/s/ or /z/), Z forhzi(/µ/ or /dz/). While we have manually recon- ductedhsi,hziand a few other marginal ambiguous graphemes to the actual phonemes they correspond to, forheiandhoiwe have decided to keep the same neutralization as in GLAFF-IT. This choice is due to the fact that manually disambiguating all cases to reflect the actual pronunciation in the standard variety of Italian would have been very time con- suming, but it is also justified by the fact that in many varieties (including the northern ones of the authors) these distinctions are not made.

Another systematic correction concerns the

placement of stress, that for many wordforms have been obtained automatically in GLAFF-IT, and sometimes turns out not to be in the right place: for instance, in many third-plural forms, the stress is incorrectly placed on the penulti- mate (e.g.PRS.IND.3SG/divent"ano/ 'they be- come", /okkup"ano/ 'they occupy"), while in our resource we move it to the (pre)antepenultimate (e.g. /div"entano/, /"okkupano/). While in other cases it was possible to correct stress position in an automatic way, by moving the stress to the syllable where it is systematically placed (e.g. the antepenultimate in forms likePRET.IND.3SG /f"eÙero/ 'they did"), in this case, since there are two alternatives, the changes had to be done semi- automatically, by automatically moving the stress to the antepenultimate, and then manually moving it to the preantepenultimate whenever needed.

In cases of cells containing more than one word-

form, wekeeponlyoneofthecell-mates. Wherever it was possible, we have used Thornton (2008)"s description of overabundance in Italian verb inflec- tion to select the less marginal variant (e.g., keeping /d"evo/ rather than /d"ebbo/ in thePRS.IND.1SGof

DOVERE'must").

Several other punctual corrections were manu-

ally made on the data of GLAFF-IT, yielding the current version of our resource, that is clean enough to be able to perform an entropy-based analysis shedding light on the patterns of interpredictability between wordforms in Italian verb paradigms.

3 The Method

The Qumin toolkit computes implicative entropy

values estimating the uncertainty in predicting each paradigm cell assuming knowledge of one (or more than one) wordform, following the procedure de- scribed in Beniamine (2018). Here, we illustrate the methodology using the data given in Table 1.lexeme conj.

GER PRS.IND.2PLAMARE'love" 1st/am"ando/ /am"ate/

VEDERE'see" 2nd/ved"endo/ /ved"ete/

SENTIRE'hear" 3rd/sent"endo/ /sent"ite/Table 1: Italian verbs of different conjugations. The first step of the procedure consists in classi- fying verbs according to the patterns of formal al- ternation between wordforms, and the phonological context in which such alternations are attested. As is shown in the second column of Table 2, 1 stand 2 ndconjugation verbs display the same pattern (1), while 3 rdconjugation verbs use another pattern (2). The second step is another classification based on the patterns that can potentially be applied toGER to obtainPRS.IND.2PL. As can be seen in the third column of Table 2, verbs of the 2 ndand 3rdconju- gation are in the same class (B), because patterns

1 and 2 can potentially be applied to aGERend-

ing in /endo/, while only pattern 1 can be applied to 1 stconjugation verbs withGERin /ando/. En- tropy is then computed for each of the classes of this second classification, weighing the probability type frequency in the data, i.e., the number of verbs in which they are attested: here, data fromLeFFI are given in the last column of Table 2. lexeme pattern/context (1SG$3SG)applicable patternsn. verbsAMARE1 (ndo$te / V#) A (1) 1,505

VEDERE1 (ndo$te / V#) B (1,2) 320

SENTIRE2 (endo$ite / C#) B (1,2) 215

Table 2: Information used to compute the entropy

of predictingPRS.IND.2PLfromGER.

H(PRS:IND:2PLjGER)

=::1;5052;0401;5051;505log21;5051;505 :::::+5352;040320535 log2320535 +215535
log2215535 =::1;5052;0400 +5352;040 0:972 =:::0:255 (1)As isshown in Equation1, there isno uncertainty in class A: given aGERin /ando/,PRS.IND.2PLcan- not but be in /ate/. On the other hand, given aGER in /endo/,PRS.IND.2PLcan be in in /ete/ (apply- ing pattern 1) or in /ite/ (applying pattern 2). As a consequence, there is some uncertainty in this case.

The entropy values of different classes are then

summed and weighed - again on the basis of type frequency - in a single entropy value, that estimates the overall uncertainty in predictingPRS.IND.2PL fromGERin Italian verbs.

4 Results

Giving the data ofLeFFIas input to the Qumin

toolkit, the output is an entropy-based distance ma- trix of all the cells of Italian verb paradigms. We do not show it here for reasons of space as it comprises

53 columns and rows, but we use its values to draw

a mapping of the paradigm in zones of full interpre- dictability, wheretwocellsA;Bareconflatedinthe with no uncertainty, i.e. ifH(AjB) =H(BjA) =

0. The outcome of this grouping is given in Table

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